# 匯入工具包
import numpy as np
import argparse
import cv2

# 設定引數

def order_points(pts):
    # 一共4個座標點
    rect = np.zeros((4, 2), dtype = "float32")

    # 按順序找到對應座標0123分別是 左上，右上，右下，左下
    # 計算左上，右下
    s = pts.sum(axis = 1)
    rect[0] = pts[np.argmin(s)]
    rect[2] = pts[np.argmax(s)]

    # 計算右上和左下
    diff = np.diff(pts, axis = 1)
    rect[
 
1] = pts[np.argmin(diff)]
    rect[3] = pts[np.argmax(diff)]

    return rect

def four_point_transform(image, pts):
    # 獲取輸入座標點
    rect = order_points(pts)
    (tl, tr, br, bl) = rect

    # 計算輸入的w和h值
    widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
    widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
    maxWidth 
 
= max(int(widthA), int(widthB))

    heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
    heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
    maxHeight = max(int(heightA), int(heightB))

    # 變換後對應座標位置
    dst = np.array([
        [0, 0],
        [maxWidth - 1, 0],
        [maxWidth 
 
- 1, maxHeight - 1],
        [0, maxHeight - 1]], dtype = "float32")

    # 計算變換矩陣
    M = cv2.getPerspectiveTransform(rect, dst)
    warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))

    # 返回變換後結果
    return warped

def resize(image, width=None, height=None, inter=cv2.INTER_AREA):
    dim = None
    (h, w) = image.shape[:2]
    if width is None and height is None:
        return image
    if width is None:
        r = height / float(h)
        dim = (int(w * r), height)
    else:
        r = width / float(w)
        dim = (width, int(h * r))
    resized = cv2.resize(image, dim, interpolation=inter)
    return resized

# 讀取輸入
image = cv2.imread(r'C:\Users\asus\Desktop\receipt.jpg')
#座標也會相同變化
ratio = image.shape[0] / 500.0
orig = image.copy()


image = resize(orig, height = 500)

# 預處理
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(gray, 75, 200)

# 展示預處理結果
print("STEP 1: 邊緣檢測")
cv2.imshow("Image", image)
cv2.imshow("Edged", edged)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 輪廓檢測
cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[0]
cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:5]

# 遍歷輪廓
for c in cnts:
    # 計算輪廓近似
    peri = cv2.arcLength(c, True)
    # C表示輸入的點集
    # epsilon表示從原始輪廓到近似輪廓的最大距離，它是一個準確度引數
    # True表示封閉的
    approx = cv2.approxPolyDP(c, 0.02 * peri, True)

    # 4個點的時候就拿出來
    if len(approx) == 4:
        screenCnt = approx
        break

# 展示結果
print("STEP 2: 獲取輪廓")
cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)
cv2.imshow("Outline", image)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 透視變換
warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)

# 二值處理
warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
ref = cv2.threshold(warped, 100, 255, cv2.THRESH_BINARY)[1]
cv2.imwrite('scan.jpg', ref)
# 展示結果
print("STEP 3: 變換")
cv2.imshow("Original", resize(orig, height = 650))
cv2.imshow("Scanned", resize(ref, height = 650))
cv2.waitKey(0)